IL311528A - Gan-cnn for mhc peptide binding prediction - Google Patents
Gan-cnn for mhc peptide binding predictionInfo
- Publication number
- IL311528A IL311528A IL311528A IL31152824A IL311528A IL 311528 A IL311528 A IL 311528A IL 311528 A IL311528 A IL 311528A IL 31152824 A IL31152824 A IL 31152824A IL 311528 A IL311528 A IL 311528A
- Authority
- IL
- Israel
- Prior art keywords
- mhc
- polypeptide
- positive
- cnn
- gan
- Prior art date
Links
- 108090000765 processed proteins & peptides Proteins 0.000 title claims 5
- 230000003993 interaction Effects 0.000 claims 36
- 238000013527 convolutional neural network Methods 0.000 claims 25
- 238000000034 method Methods 0.000 claims 15
- 108700028369 Alleles Proteins 0.000 claims 13
- 229920001184 polypeptide Polymers 0.000 claims 4
- 102000004196 processed proteins & peptides Human genes 0.000 claims 4
- 102100028972 HLA class I histocompatibility antigen, A alpha chain Human genes 0.000 claims 1
- 102100028976 HLA class I histocompatibility antigen, B alpha chain Human genes 0.000 claims 1
- 102100028971 HLA class I histocompatibility antigen, C alpha chain Human genes 0.000 claims 1
- 108010075704 HLA-A Antigens Proteins 0.000 claims 1
- 108010058607 HLA-B Antigens Proteins 0.000 claims 1
- 108010052199 HLA-C Antigens Proteins 0.000 claims 1
- 108700018351 Major Histocompatibility Complex Proteins 0.000 claims 1
- 206010028980 Neoplasm Diseases 0.000 claims 1
- 125000003275 alpha amino acid group Chemical group 0.000 claims 1
- 239000000427 antigen Substances 0.000 claims 1
- 108091007433 antigens Proteins 0.000 claims 1
- 102000036639 antigens Human genes 0.000 claims 1
- 108090000623 proteins and genes Proteins 0.000 claims 1
- 102000004169 proteins and genes Human genes 0.000 claims 1
- 230000002194 synthesizing effect Effects 0.000 claims 1
Classifications
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- G16B40/20—Supervised data analysis
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/70—Machine learning, data mining or chemometrics
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
- G16B20/30—Detection of binding sites or motifs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/094—Adversarial learning
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
- G16B30/10—Sequence alignment; Homology search
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/30—Prediction of properties of chemical compounds, compositions or mixtures
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/40—Searching chemical structures or physicochemical data
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/50—Molecular design, e.g. of drugs
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/90—Programming languages; Computing architectures; Database systems; Data warehousing
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C60/00—Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C99/00—Subject matter not provided for in other groups of this subclass
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Computing Systems (AREA)
- Bioinformatics & Computational Biology (AREA)
- Chemical & Material Sciences (AREA)
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Crystallography & Structural Chemistry (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Data Mining & Analysis (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Biophysics (AREA)
- Databases & Information Systems (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Biotechnology (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Molecular Biology (AREA)
- Analytical Chemistry (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Genetics & Genomics (AREA)
- Medicinal Chemistry (AREA)
- Pharmacology & Pharmacy (AREA)
- Epidemiology (AREA)
- Bioethics (AREA)
- Public Health (AREA)
- Biomedical Technology (AREA)
- Computational Linguistics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Investigating Or Analysing Biological Materials (AREA)
- Peptides Or Proteins (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Claims (16)
1. A computer-implemented method for classifying data, comprising: presenting, by a computing device, a dataset to a convolutional neural network (CNN), wherein the dataset comprises a plurality of candidate polypeptide-MHC-I interactions, and wherein the CNN is trained based on positive simulated polypeptide-major histocompatibility complex class I (MHC-I) interaction data, positive real polypeptide-MHC-I interaction data, and negative real polypeptide-MHC-I interaction data; and classifying, by the CNN, at least one candidate polypeptide-MHC-I interaction of the plurality of candidate polypeptide-MHC-I interactions as positive or negative.
2. The computer-implemented method of claim 1, further comprising: a. generating, via a GAN generator, increasingly accurate positive simulated polypeptide-MHC-I interaction data until a GAN discriminator classifies the positive simulated polypeptide-MHC-I interaction data as positive; b. presenting the positive simulated polypeptide-MHC-I interaction data, positive real polypeptide-MHC-I interaction data, and negative real polypeptide-MHC-I interaction data to the CNN, until the CNN classifies each type of data as positive or negative; c. presenting the positive real data and the negative real data to the CNN to generate prediction scores; and d. determining based on the prediction scores, whether the GAN is trained or not trained, and when the GAN is not trained, repeating steps a-c until a determination is made, based on the prediction scores, that the GAN is trained.
3. The computer-implemented method of claim 2, wherein generating the increasingly accurate positive simulated polypeptide-MHC-I interaction data until the GAN discriminator classifies the positive simulated polypeptide-MHC-I interaction data as positive comprises: e. generating, by the GAN generator according to a set of GAN parameters, a first simulated dataset comprising simulated positive polypeptide-MHC-I interactions for a MHC allele; f. combining the first simulated dataset with the positive real polypeptide-MHC-I interactions for the MHC allele, and the negative real polypeptide-MHC-I interactions for the MHC allele to create a GAN training dataset; g. determining, by a discriminator according to a decision boundary, whether a respective polypeptide-MHC-I interaction for the MHC allele in the GAN training dataset is simulated positive, real positive, or real negative; h. adjusting, based on accuracy of the determination by the discriminator, one or more of the set of GAN parameters or the decision boundary; and i. repeating steps e-h until a first stop criterion is satisfied.
4. The computer-implemented method of claim 3, wherein presenting the positive simulated polypeptide-MHC-I interaction data, the positive real polypeptide- MHC-I interaction data, and the negative real polypeptide-MHC-I interaction data to the convolutional neural network (CNN), until the CNN classifies respective polypeptide- MHC-I interaction data as positive or negative comprises: j. generating, by the GAN generator according to the set of GAN parameters, a second simulated dataset comprising simulated positive polypeptide-MHC-I interactions for the MHC allele; k. combining the second simulated dataset, the positive real polypeptide-MHC-I interactions for the MHC allele, and the negative real polypeptide-MHC-I interactions for the MHC allele to create a CNN training dataset; l. presenting the CNN training dataset to the convolutional neural network (CNN); m. classifying, by the CNN according to a set of CNN parameters, a respective polypeptide-MHC-I interaction for the MHC allele in the CNN training dataset as positive or negative; n. adjusting, based on accuracy of the classification by the CNN, one or more of the set of CNN parameters; and o. repeating steps l-n until a second stop criterion is satisfied.
5. The computer-implemented of claim 4, wherein presenting the positive real polypeptide-MHC-I interaction data and the negative real polypeptide-MHC-I interaction data to the CNN to generate prediction scores comprises: classifying, by the CNN according to the set of CNN parameters, a respective polypeptide-MHC-I interaction for the MHC allele as positive or negative.
6. The computer-implemented method of claim 5, wherein determining, based on the prediction scores, whether the GAN is trained comprises determining accuracy of the classification by the CNN, wherein when the accuracy of the classification satisfies a third stop criterion, outputting the GAN and the CNN.
7. The computer-implemented method of claim 5, wherein determining, based on the prediction scores, whether the GAN is trained comprises determining accuracy of the classification by the CNN, wherein when the accuracy of the classification does not satisfy a third stop criterion, returning to step a.
8. The computer-implemented method of claim 3, wherein the set of GAN parameters comprises one or more of allele type, allele length, generating category, model complexity, learning rate, or batch size.
9. The computer-implemented method of claim 8, wherein the allele type comprises one or more of HLA-A, HLA-B, HLA-C, or a subtype thereof.
10. The computer-implemented method of claim 1, further comprising: synthesizing a polypeptide from the at least one candidate polypeptide- MHC-I interaction classified as a positive polypeptide-MHC-I interaction.
11. A polypeptide produced by the method of claim 10.
12. The computer-implemented method of claim 10, wherein the polypeptide is a tumor specific antigen.
13. The computer-implemented method of claim 10, wherein the polypeptide comprises an amino acid sequence that specifically binds to an MHC-I protein encoded by a selected MHC allele.
14. The computer-implemented method of claim 2, wherein generating the increasingly accurate positive simulated polypeptide-MHC-I interaction data until the GAN discriminator classifies the positive simulated polypeptide-MHC-I interaction data as positive comprises: iteratively executing the GAN discriminator in order to increase a likelihood of giving a high probability to positive real polypeptide-MHC-I interaction data, a low probability to the positive simulated polypeptide-MHC-I interaction data, and a low probability to the negative real polypeptide-MHC-I interaction data; and iteratively executing the GAN generator in order to increase a probability of the positive simulated polypeptide-MHC-I interaction data being rated highly.
15. An apparatus configured for performing the method of any one of claims 1- and 14.
16. A computer readable medium (CRM) configured for performing the method of any one of claims 1-9 and 14. For the Applicants, REINHOLD COHN AND PARTNERS By: Dr. Sheila Zrihan-Licht, Patent Attorney, Partner SZR
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US201862631710P | 2018-02-17 | 2018-02-17 | |
| PCT/US2019/018434 WO2019161342A1 (en) | 2018-02-17 | 2019-02-18 | Gan-cnn for mhc peptide binding prediction |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| IL311528A true IL311528A (en) | 2024-05-01 |
Family
ID=65686006
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| IL311528A IL311528A (en) | 2018-02-17 | 2019-02-18 | Gan-cnn for mhc peptide binding prediction |
| IL276730A IL276730B1 (en) | 2018-02-17 | 2019-02-18 | Gan-cnn for mhc peptide binding prediction |
Family Applications After (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| IL276730A IL276730B1 (en) | 2018-02-17 | 2019-02-18 | Gan-cnn for mhc peptide binding prediction |
Country Status (11)
| Country | Link |
|---|---|
| US (1) | US20190259474A1 (en) |
| EP (1) | EP3753022A1 (en) |
| JP (2) | JP7047115B2 (en) |
| KR (2) | KR102607567B1 (en) |
| CN (1) | CN112119464A (en) |
| AU (2) | AU2019221793B2 (en) |
| CA (1) | CA3091480A1 (en) |
| IL (2) | IL311528A (en) |
| MX (1) | MX2020008597A (en) |
| SG (1) | SG11202007854QA (en) |
| WO (1) | WO2019161342A1 (en) |
Families Citing this family (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB201718756D0 (en) * | 2017-11-13 | 2017-12-27 | Cambridge Bio-Augmentation Systems Ltd | Neural interface |
| US10706534B2 (en) * | 2017-07-26 | 2020-07-07 | Scott Anderson Middlebrooks | Method and apparatus for classifying a data point in imaging data |
| US11704573B2 (en) * | 2019-03-25 | 2023-07-18 | Here Global B.V. | Method, apparatus, and computer program product for identifying and compensating content contributors |
| US20200379814A1 (en) * | 2019-05-29 | 2020-12-03 | Advanced Micro Devices, Inc. | Computer resource scheduling using generative adversarial networks |
| CA3142888A1 (en) * | 2019-06-12 | 2020-12-17 | Quantum-Si Incorporated | Techniques for protein identification using machine learning and related systems and methods |
| CN110598786B (en) * | 2019-09-09 | 2022-01-07 | 京东方科技集团股份有限公司 | Neural network training method, semantic classification method and semantic classification device |
| US20210150270A1 (en) * | 2019-11-19 | 2021-05-20 | International Business Machines Corporation | Mathematical function defined natural language annotation |
| CN110875790A (en) * | 2019-11-19 | 2020-03-10 | 上海大学 | Wireless channel modeling implementation method based on generation countermeasure network |
| WO2021099584A1 (en) * | 2019-11-22 | 2021-05-27 | F. Hoffmann-La Roche Ag | Multiple instance learner for tissue image classification |
| JP7419534B2 (en) * | 2019-12-12 | 2024-01-22 | ジャスト－エヴォテック バイオロジクス，インコーポレイテッド | Generation of protein sequences using machine learning techniques based on template protein sequences |
| CN111063391B (en) * | 2019-12-20 | 2023-04-25 | 海南大学 | Non-culturable microorganism screening system based on generation type countermeasure network principle |
| CN111402113B (en) * | 2020-03-09 | 2021-10-15 | 北京字节跳动网络技术有限公司 | Image processing method, image processing device, electronic equipment and computer readable medium |
| WO2021195155A1 (en) * | 2020-03-23 | 2021-09-30 | Genentech, Inc. | Estimating pharmacokinetic parameters using deep learning |
| US20210295173A1 (en) * | 2020-03-23 | 2021-09-23 | Samsung Electronics Co., Ltd. | Method and apparatus for data-free network quantization and compression with adversarial knowledge distillation |
| US10902291B1 (en) * | 2020-08-04 | 2021-01-26 | Superb Ai Co., Ltd. | Methods for training auto labeling device and performing auto labeling related to segmentation while performing automatic verification by using uncertainty scores and devices using the same |
| US10885387B1 (en) * | 2020-08-04 | 2021-01-05 | SUPERB Al CO., LTD. | Methods for training auto-labeling device and performing auto-labeling by using hybrid classification and devices using the same |
| EP4205125A4 (en) * | 2020-08-28 | 2024-02-21 | Just-Evotec Biologics, Inc. | Implementing a generative machine learning architecture to produce training data for a classification model |
| CN112597705B (en) * | 2020-12-28 | 2022-05-24 | 哈尔滨工业大学 | Multi-feature health factor fusion method based on SCVNN |
| CN112309497B (en) * | 2020-12-28 | 2021-04-02 | 武汉金开瑞生物工程有限公司 | Method and device for predicting protein structure based on Cycle-GAN |
| KR102519341B1 (en) * | 2021-03-18 | 2023-04-06 | 재단법인한국조선해양기자재연구원 | Early detection system for uneven tire wear by real-time noise analysis and method thereof |
| US20220328127A1 (en) * | 2021-04-05 | 2022-10-13 | Nec Laboratories America, Inc. | Peptide based vaccine generation system with dual projection generative adversarial networks |
| US20220319635A1 (en) * | 2021-04-05 | 2022-10-06 | Nec Laboratories America, Inc. | Generating minority-class examples for training data |
| US20230083313A1 (en) * | 2021-09-13 | 2023-03-16 | Nec Laboratories America, Inc. | Peptide search system for immunotherapy |
| KR102507111B1 (en) * | 2022-03-29 | 2023-03-07 | 주식회사 네오젠티씨 | Apparatus and method for determining reliability of immunopeptidome information |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8121797B2 (en) * | 2007-01-12 | 2012-02-21 | Microsoft Corporation | T-cell epitope prediction |
| US9805305B2 (en) * | 2015-08-07 | 2017-10-31 | Yahoo Holdings, Inc. | Boosted deep convolutional neural networks (CNNs) |
| WO2018022752A1 (en) | 2016-07-27 | 2018-02-01 | James R. Glidewell Dental Ceramics, Inc. | Dental cad automation using deep learning |
| CN106845471A (en) * | 2017-02-20 | 2017-06-13 | 深圳市唯特视科技有限公司 | A kind of vision significance Forecasting Methodology based on generation confrontation network |
| CN107590518A (en) | 2017-08-14 | 2018-01-16 | 华南理工大学 | A kind of confrontation network training method of multiple features study |
-
2019
- 2019-02-18 SG SG11202007854QA patent/SG11202007854QA/en unknown
- 2019-02-18 AU AU2019221793A patent/AU2019221793B2/en active Active
- 2019-02-18 CN CN201980025487.XA patent/CN112119464A/en active Pending
- 2019-02-18 CA CA3091480A patent/CA3091480A1/en active Pending
- 2019-02-18 MX MX2020008597A patent/MX2020008597A/en unknown
- 2019-02-18 KR KR1020207026559A patent/KR102607567B1/en active Application Filing
- 2019-02-18 JP JP2020543800A patent/JP7047115B2/en active Active
- 2019-02-18 KR KR1020237040230A patent/KR20230164757A/en active Search and Examination
- 2019-02-18 EP EP19709215.8A patent/EP3753022A1/en active Pending
- 2019-02-18 IL IL311528A patent/IL311528A/en unknown
- 2019-02-18 IL IL276730A patent/IL276730B1/en unknown
- 2019-02-18 US US16/278,611 patent/US20190259474A1/en active Pending
- 2019-02-18 WO PCT/US2019/018434 patent/WO2019161342A1/en active Application Filing
-
2022
- 2022-03-23 JP JP2022046973A patent/JP7459159B2/en active Active
- 2022-08-26 AU AU2022221568A patent/AU2022221568B2/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| MX2020008597A (en) | 2020-12-11 |
| JP7459159B2 (en) | 2024-04-01 |
| RU2020130420A (en) | 2022-03-17 |
| CA3091480A1 (en) | 2019-08-22 |
| RU2020130420A3 (en) | 2022-03-17 |
| AU2022221568A1 (en) | 2022-09-22 |
| EP3753022A1 (en) | 2020-12-23 |
| AU2019221793A1 (en) | 2020-09-17 |
| KR20200125948A (en) | 2020-11-05 |
| AU2019221793B2 (en) | 2022-09-15 |
| IL276730B1 (en) | 2024-04-01 |
| KR20230164757A (en) | 2023-12-04 |
| SG11202007854QA (en) | 2020-09-29 |
| KR102607567B1 (en) | 2023-12-01 |
| AU2022221568B2 (en) | 2024-06-13 |
| WO2019161342A1 (en) | 2019-08-22 |
| US20190259474A1 (en) | 2019-08-22 |
| JP7047115B2 (en) | 2022-04-04 |
| JP2022101551A (en) | 2022-07-06 |
| CN112119464A (en) | 2020-12-22 |
| IL276730A (en) | 2020-09-30 |
| JP2021514086A (en) | 2021-06-03 |
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